With the rapid development of medical data sharing,issues of privacy and ownership have become prominent,which have limited the scale of data sharing.To address the above challenges,we propose a blockchainbased data-s...With the rapid development of medical data sharing,issues of privacy and ownership have become prominent,which have limited the scale of data sharing.To address the above challenges,we propose a blockchainbased data-sharing framework to ensure data security and encourage data owners to actively participate in sharing.We introduce a reliable attribute-based searchable encryption scheme that enables fine-grained access control of encrypted data and ensures secure and efficient data sharing.The revenue distribution model is constructed based on Shapley value to motivate participants.Additionally,by integrating the smart contract technology of blockchain,the search operation and incentive mechanism are automatically executed.Through revenue distribution analysis,the incentive effect and rationality of the proposed scheme are verified.Performance evaluation shows that,compared with traditional data-sharing models,our proposed framework not only meets data security requirements but also incentivizes more participants to actively participate in data sharing.展开更多
As the integration of medical big data and artificial intelligence advances,the secure sharing of medical data has become a key driving force for advancing disease research and clinical diagnosis.Federated learning,a ...As the integration of medical big data and artificial intelligence advances,the secure sharing of medical data has become a key driving force for advancing disease research and clinical diagnosis.Federated learning,a distributed approach enabling collaborative data processing without sharing raw data,offers promising solutions to challenges in multi-center medical data sharing.This review summarizes the progress of federated learning in multi-center medical data processing,analyzed from four perspectives:system architectures,data distribution strategies,clinical tasks,and algorithmic models.At the same time,this paper explores the challenges in practical applications,such as data heterogeneity,communication overhead,and privacy concerns.It proposes driving future research development by optimizing algorithms,strengthening privacy protection mechanisms,and enhancing computational efficiency.展开更多
As the volume of healthcare and medical data increases from diverse sources,real-world scenarios involving data sharing and collaboration have certain challenges,including the risk of privacy leakage,difficulty in dat...As the volume of healthcare and medical data increases from diverse sources,real-world scenarios involving data sharing and collaboration have certain challenges,including the risk of privacy leakage,difficulty in data fusion,low reliability of data storage,low effectiveness of data sharing,etc.To guarantee the service quality of data collaboration,this paper presents a privacy-preserving Healthcare and Medical Data Collaboration Service System combining Blockchain with Federated Learning,termed FL-HMChain.This system is composed of three layers:Data extraction and storage,data management,and data application.Focusing on healthcare and medical data,a healthcare and medical blockchain is constructed to realize data storage,transfer,processing,and access with security,real-time,reliability,and integrity.An improved master node selection consensus mechanism is presented to detect and prevent dishonest behavior,ensuring the overall reliability and trustworthiness of the collaborative model training process.Furthermore,healthcare and medical data collaboration services in real-world scenarios have been discussed and developed.To further validate the performance of FL-HMChain,a Convolutional Neural Network-based Federated Learning(FL-CNN-HMChain)model is investigated for medical image identification.This model achieves better performance compared to the baseline Convolutional Neural Network(CNN),having an average improvement of 4.7%on Area Under Curve(AUC)and 7%on Accuracy(ACC),respectively.Furthermore,the probability of privacy leakage can be effectively reduced by the blockchain-based parameter transfer mechanism in federated learning between local and global models.展开更多
Medical blockchain data-sharing is a technique that employs blockchain technology to facilitate the sharing of electronic medical data.The blockchain is a decentralized digital ledger that ensures data-sharing securit...Medical blockchain data-sharing is a technique that employs blockchain technology to facilitate the sharing of electronic medical data.The blockchain is a decentralized digital ledger that ensures data-sharing security,transparency,and traceability through cryptographic technology and consensus algorithms.Consequently,medical blockchain data-sharing methods have garnered significant attention and research efforts.Nevertheless,current methods have different storage and transmission measures for original data in the medical blockchain,resulting in large differences in performance and privacy.Therefore,we divide the medical blockchain data-sharing method into on-chain sharing and off-chain sharing according to the original data storage location.Among them,off-chain sharing can be subdivided into on-cloud sharing and local sharing according to whether the data is moved.Subsequently,we provide a detailed analysis of basic processes and research content for each method.Finally,we summarize the challenges posed by the current methods and discuss future research directions.展开更多
The identification and mitigation of anomaly data,characterized by deviations from normal patterns or singularities,stand as critical endeavors in modern technological landscapes,spanning domains such as Non-Fungible ...The identification and mitigation of anomaly data,characterized by deviations from normal patterns or singularities,stand as critical endeavors in modern technological landscapes,spanning domains such as Non-Fungible Tokens(NFTs),cyber-security,and the burgeoning metaverse.This paper presents a novel proposal aimed at refining anomaly detection methodologies,with a particular focus on continuous data streams.The essence of the proposed approach lies in analyzing the rate of change within such data streams,leveraging this dynamic aspect to discern anomalies with heightened precision and efficacy.Through empirical evaluation,our method demonstrates a marked improvement over existing techniques,showcasing more nuanced and sophisticated result values.Moreover,we envision a trajectory of continuous research and development,wherein iterative refinement and supplementation will tailor our approach to various anomaly detection scenarios,ensuring adaptability and robustness in real-world applications.展开更多
Eye state classification acts as a vital part of the biomedical sector,for instance,smart home device control,drowsy driving recognition,and so on.The modifications in the cognitive levels can be reflected via transformi...Eye state classification acts as a vital part of the biomedical sector,for instance,smart home device control,drowsy driving recognition,and so on.The modifications in the cognitive levels can be reflected via transforming the electro-encephalogram(EEG)signals.The deep learning(DL)models automated extract the features and often showcased improved outcomes over the conventional clas-sification model in the recognition processes.This paper presents an Ensemble Deep Learning with Chimp Optimization Algorithm for EEG Eye State Classifi-cation(EDLCOA-ESC).The proposed EDLCOA-ESC technique involves min-max normalization approach as a pre-processing step.Besides,wavelet packet decomposition(WPD)technique is employed for the extraction of useful features from the EEG signals.In addition,an ensemble of deep sparse autoencoder(DSAE)and kernel ridge regression(KRR)models are employed for EEG Eye State classification.Finally,hyperparameters tuning of the DSAE model takes place using COA and thereby boost the classification results to a maximum extent.An extensive range of simulation analysis on the benchmark dataset is car-ried out and the results reported the promising performance of the EDLCOA-ESC technique over the recent approaches with maximum accuracy of 98.50%.展开更多
Medical data classification(MDC)refers to the application of classification methods on medical datasets.This work focuses on applying a classification task to medical datasets related to specific diseases in order to ...Medical data classification(MDC)refers to the application of classification methods on medical datasets.This work focuses on applying a classification task to medical datasets related to specific diseases in order to predict the associated diagnosis or prognosis.To gain experts’trust,the prediction and the reasoning behind it are equally important.Accordingly,we confine our research to learn rule-based models because they are transparent and comprehensible.One approach to MDC involves the use of metaheuristic(MH)algorithms.Here we report on the development and testing of a novel MH algorithm:IWD-Miner.This algorithm can be viewed as a fusion of Intelligent Water Drops(IWDs)and AntMiner+.It was subjected to a four-stage sensitivity analysis to optimize its performance.For this purpose,21 publicly available medical datasets were used from the Machine Learning Repository at the University of California Irvine.Interestingly,there were only limited differences in performance between IWDMiner variants which is suggestive of its robustness.Finally,using the same 21 datasets,we compared the performance of the optimized IWD-Miner against two extant algorithms,AntMiner+and J48.The experiments showed that both rival algorithms are considered comparable in the effectiveness to IWD-Miner,as confirmed by the Wilcoxon nonparametric statistical test.Results suggest that IWD-Miner is more efficient than AntMiner+as measured by the average number of fitness evaluations to a solution(1,386,621.30 vs.2,827,283.88 fitness evaluations,respectively).J48 exhibited higher accuracy on average than IWD-Miner(79.58 vs.73.65,respectively)but produced larger models(32.82 leaves vs.8.38 terms,respectively).展开更多
Recently,medical data classification becomes a hot research topic among healthcare professionals and research communities,which assist in the disease diagnosis and decision making process.The latest developments of ar...Recently,medical data classification becomes a hot research topic among healthcare professionals and research communities,which assist in the disease diagnosis and decision making process.The latest developments of artificial intelligence(AI)approaches paves a way for the design of effective medical data classification models.At the same time,the existence of numerous features in the medical dataset poses a curse of dimensionality problem.For resolving the issues,this article introduces a novel feature subset selection with artificial intelligence based classification model for biomedical data(FSS-AICBD)technique.The FSS-AICBD technique intends to derive a useful set of features and thereby improve the classifier results.Primarily,the FSS-AICBD technique undergoes min-max normalization technique to prevent data complexity.In addition,the information gain(IG)approach is applied for the optimal selection of feature subsets.Also,group search optimizer(GSO)with deep belief network(DBN)model is utilized for biomedical data classification where the hyperparameters of the DBN model can be optimally tuned by the GSO algorithm.The choice of IG and GSO approaches results in promising medical data classification results.The experimental result analysis of the FSS-AICBD technique takes place using different benchmark healthcare datasets.The simulation results reported the enhanced outcomes of the FSS-AICBD technique interms of several measures.展开更多
Medical data classification becomes a hot research topic in the healthcare sector to aid physicians in the healthcare sector for decision making.Besides,the advances of machine learning(ML)techniques assist to perform...Medical data classification becomes a hot research topic in the healthcare sector to aid physicians in the healthcare sector for decision making.Besides,the advances of machine learning(ML)techniques assist to perform the effective classification task.With this motivation,this paper presents a Fuzzy Clustering Approach Based on Breadth-first Search Algorithm(FCA-BFS)with optimal support vector machine(OSVM)model,named FCABFS-OSVM for medical data classification.The proposed FCABFS-OSVM technique intends to classify the healthcare data by the use of clustering and classification models.Besides,the proposed FCABFSOSVM technique involves the design of FCABFS technique to cluster the medical data which helps to boost the classification performance.Moreover,the OSVM model investigates the clustered medical data to perform classification process.Furthermore,Archimedes optimization algorithm(AOA)is utilized to the SVM parameters and boost the medical data classification results.A wide range of simulations takes place to highlight the promising performance of the FCABFS-OSVM technique.Extensive comparison studies reported the enhanced outcomes of the FCABFS-OSVM technique over the recent state of art approaches.展开更多
This study presents a novelmethod to detect themedical application based on Quantum Computing(QC)and a few Machine Learning(ML)systems.QC has a primary advantage i.e.,it uses the impact of quantum parallelism to provi...This study presents a novelmethod to detect themedical application based on Quantum Computing(QC)and a few Machine Learning(ML)systems.QC has a primary advantage i.e.,it uses the impact of quantum parallelism to provide the consequences of prime factorization issue in a matter of seconds.So,this model is suggested for medical application only by recent researchers.A novel strategy i.e.,Quantum KernelMethod(QKM)is proposed in this paper for data prediction.In this QKM process,Linear Tunicate Swarm Algorithm(LTSA),the optimization technique is used to calculate the loss function initially and is aimed at medical data.The output of optimization is either 0 or 1 i.e.,odd or even in QC.From this output value,the data is identified according to the class.Meanwhile,the method also reduces time,saves cost and improves the efficiency by feature selection process i.e.,Filter method.After the features are extracted,QKM is deployed as a classification model,while the loss function is minimized by LTSA.The motivation of the minimal objective is to remain faster.However,some computations can be performed more efficiently by the proposed model.In testing,the test data was evaluated by minimal loss function.The outcomes were assessed in terms of accuracy,computational time,and so on.For this,databases like Lymphography,Dermatology,and Arrhythmia were used.展开更多
With the exponential developments of wireless networking and inexpensive Internet of Things(IoT),a wide range of applications has been designed to attain enhanced services.Due to the limited energy capacity of IoT dev...With the exponential developments of wireless networking and inexpensive Internet of Things(IoT),a wide range of applications has been designed to attain enhanced services.Due to the limited energy capacity of IoT devices,energy-aware clustering techniques can be highly preferable.At the same time,artificial intelligence(AI)techniques can be applied to perform appropriate disease diagnostic processes.With this motivation,this study designs a novel squirrel search algorithm-based energy-aware clustering with a medical data classification(SSAC-MDC)model in an IoT environment.The goal of the SSAC-MDC technique is to attain maximum energy efficiency and disease diagnosis in the IoT environment.The proposed SSAC-MDC technique involves the design of the squirrel search algorithm-based clustering(SSAC)technique to choose the proper set of cluster heads(CHs)and construct clusters.Besides,the medical data classification process involves three different subprocesses namely pre-processing,autoencoder(AE)based classification,and improved beetle antenna search(IBAS)based parameter tuning.The design of the SSAC technique and IBAS based parameter optimization processes show the novelty of the work.For show-casing the improved performance of the SSAC-MDC technique,a series of experiments were performed and the comparative results highlighted the supremacy of the SSAC-MDC technique over the recent methods.展开更多
With the improvement of current online communication schemes,it is now possible to successfully distribute and transport secured digital Content via the communication channel at a faster transmission rate.Traditional ...With the improvement of current online communication schemes,it is now possible to successfully distribute and transport secured digital Content via the communication channel at a faster transmission rate.Traditional steganography and cryptography concepts are used to achieve the goal of concealing secret Content on a media and encrypting it before transmission.Both of the techniques mentioned above aid in the confidentiality of feature content.The proposed approach concerns secret content embodiment in selected pixels on digital image layers such as Red,Green,and Blue.The private Content originated from a medical client and was forwarded to a medical practitioner on the server end through the internet.The K-Means clustering principle uses the contouring approach to frame the pixel clusters on the image layers.The content embodiment procedure is performed on the selected pixel groups of all layers of the image using the Least Significant Bit(LSB)substitution technique to build the secret Content embedded image known as the stego image,which is subsequently transmitted across the internet medium to the server end.The experimental results are computed using the inputs from“Open-Access Medical Image Repositories(aylward.org)”and demonstrate the scheme’s impudence as the Content concealing procedure progresses.展开更多
In order to secure the massive heterogeneous medical data for the complex scenarios and improve the information sharing efficiency in healthcare system,a distributed medical data ledger model(DMDL)is proposed in this ...In order to secure the massive heterogeneous medical data for the complex scenarios and improve the information sharing efficiency in healthcare system,a distributed medical data ledger model(DMDL)is proposed in this paper.This DMDL model has adopted the blockchain technology including the function decoupling,the distributed consensus,smart contract as well as multi-channel communication structure of consortium blockchain.The DMDL model not only has high adaptability,but also meets the requirements of the medical treatment processes which generally involve multientities,highly private information and secure transaction.The steps for processing the medical data are also introduced.Additionally,the methods for the definition and application of the DMDL model are presented for three specific medical scenarios,i.e.,the management of the heterogeneous data,copyright protection for medical data and the secure utilization of sensitive data.The advantage of the proposed DMDL model is demonstrated by comparing with the models which are being currently adopted in healthcare system.展开更多
The Corona Virus Disease 2019(COVID-19) pandemic has taught us many valuable lessons regarding the importance of our physical and mental health. Even with so many technological advancements, we still lag in developing...The Corona Virus Disease 2019(COVID-19) pandemic has taught us many valuable lessons regarding the importance of our physical and mental health. Even with so many technological advancements, we still lag in developing a system that can fully digitalize the medical data of each individual and make it readily accessible for both the patient and health worker at any point in time. Moreover, there are also no ways for the government to identify the legitimacy of a particular clinic. This study merges modern technology with traditional approaches,thereby highlighting a scenario where artificial intelligence(AI) merges with traditional Chinese medicine(TCM), proposing a way to advance the conventional approaches. The main objective of our research is to provide a one-stop platform for the government, doctors,nurses, and patients to access their data effortlessly. The proposed portal will also check the doctors’ authenticity. Data is one of the most critical assets of an organization, so a breach of data can risk users’ lives. Data security is of primary importance and must be prioritized. The proposed methodology is based on cloud computing technology which assures the security of the data and avoids any kind of breach. The study also accounts for the difficulties encountered in creating such an infrastructure in the cloud and overcomes the hurdles faced during the project, keeping enough room for possible future innovations. To summarize, this study focuses on the digitalization of medical data and suggests some possible ways to achieve it. Moreover, it also focuses on some related aspects like security and potential digitalization difficulties.展开更多
The vast potential of medical big data to enhance healthcare outcomes remains underutilized due to privacy concerns,which restrict cross-center data sharing and the construction of diverse,large-scale datasets.To addr...The vast potential of medical big data to enhance healthcare outcomes remains underutilized due to privacy concerns,which restrict cross-center data sharing and the construction of diverse,large-scale datasets.To address this challenge,we developed a deep generative model aimed at synthesizing medical data to overcome data sharing barriers,with a focus on breast ultrasound(US)image synthesis.Specifically,we introduce CoLDiT,a conditional latent diffusion model with a transformer backbone,to generate US images of breast lesions across various Breast Imaging Reporting and Data System(BI-RADS)categories.Using a training dataset of 9,705 US images from 5,243 patients across 202 hospitals with diverse US systems,CoLDiT generated breast US images without duplicating private information,as confirmed through nearest-neighbor analysis.Blinded reader studies further validated the realism of these images,with area under the receiver operating characteristic curve(AUC)scores ranging from 0.53 to 0.77.Additionally,synthetic breast US images effectively augmented the training dataset for BI-RADS classification,achieving performance comparable to that using an equal-sized training set comprising solely real images(P=0.81 for AUC).Our findings suggest that synthetic data,such as CoLDiT-generated images,offer a viable,privacy-preserving solution to facilitate secure medical data sharing and advance the utilization of medical big data.展开更多
With the rapid evolution of artificial intelligence(AI)technologies,the medical industry is undergoing a profound transformation driven by data intelligence.As the foundational element for intelligent diagnosis,precis...With the rapid evolution of artificial intelligence(AI)technologies,the medical industry is undergoing a profound transformation driven by data intelligence.As the foundational element for intelligent diagnosis,precision prevention,and public health governance,medical data is characterized by massive volume,complex structure,diverse sources,high dimensionality,strong privacy,and high timeliness.Traditional data analysis methods are no longer sufficient to meet the comprehensive requirements of data security,intelligent processing,and decision support.Through techniques such as machine learning,deep learning,natural language processing,and multimodal fusion,AI provides robust technical support for medical data cleaning,governance,mining,and application.At the data level,intelligent algorithms enable the standardization,structuring,and interoperability of medical data,promoting information sharing across medical systems.At the model level,AI supports auxiliary diagnosis and precision treatment through image recognition,medical record analysis,and knowledge graph construction.At the system level,intelligent decision-support platforms continuously enhance the efficiency and accuracy of healthcare services.However,the widespread adoption of AI in medicine still faces challenges such as privacy protection,data security,model interpretability,and the lack of unified industry standards.Based on a systematic review of AI’s key supporting technologies in medical data processing and application,this paper focuses on the compliance challenges and adaptation strategies during industry integration and proposes an adaptation framework centered on“technological trustworthiness,data security,and industry collaboration.”The study provides theoretical and practical insights for promoting the standardized and sustainable development of AI in the healthcare industry.展开更多
Purpose:Explore the factors affecting medical data sharing in clinical research scenarios from the user’s perspective,reveal the differences between different user groups,and deepen the understanding of medical data ...Purpose:Explore the factors affecting medical data sharing in clinical research scenarios from the user’s perspective,reveal the differences between different user groups,and deepen the understanding of medical data sharing mechanisms.Design/methodology/approach:By integrating the UTAUT model,trust theory and self-efficacy theory,introducing the concepts of data transparency and individual innovation,and combining internal and external motivators,we constructed a conceptual model of medical data users’sharing behavior in clinical research scenarios.We conducted empirical research by collecting 360 pieces of first-hand data from clinical researchers.Findings:Among the internal motivators,effort expectation had a higher impact on sharing intention than performance expectation,individual innovation and self-efficacy had a higher impact on sharing behavior than trust.Trust does not show a significant impact on sharing intention,but it has a significant positive influence on sharing behavior.Among the external motivators,community influence and data transparency both positively affect sharing intention.In addition,users with different working years,professional status,data level needs,and different sharing experiences showed significant differences in healthcare data sharing.Research limitations:Our sample of clinical researchers from China was used as empirical data.Further research is needed to examine the generality of the study findings.Practical implications:The findings enhance healthcare data stakeholders’understanding of healthcare data sharing in clinical research scenarios and provide theoretical and practical insights for relevant researchers.Originality/value:In this study,the UTAUT model,trust theory and self-efficacy theory were integrated and applied to clinical research scenarios for the first time,and the concepts of data transparency and individual innovation were introduced,and the CRS-USB conceptual model was constructed and validated to extend the UTAUT model.展开更多
Vertical Federated Learning(VFL)has many applications in the field of smart healthcare with excellent performance.However,current VFL systems usually primarily focus on the privacy protection during model training,whi...Vertical Federated Learning(VFL)has many applications in the field of smart healthcare with excellent performance.However,current VFL systems usually primarily focus on the privacy protection during model training,while the preparation of training data receives little attention.In real-world applications,like smart healthcare,the process of the training data preparation may involve some participant's intention which could be privacy information for this partici-pant.To protect the privacy of the model training intention,we describe the idea of Intention-Hiding Vertical Feder-ated Learning(IHVFL)and illustrate a framework to achieve this privacy-preserving goal.First,we construct two secure screening protocols to enhance the privacy protection in feature engineering.Second,we implement the work of sample alignment bases on a novel private set intersection protocol.Finally,we use the logistic regression algorithm to demonstrate the process of IHVFL.Experiments show that our model can perform better efficiency(less than 5min)and accuracy(97%)on Breast Cancer medical dataset while maintaining the intention-hiding goal.展开更多
On April 26,2025,the Second Tsinghua Medicine Journal Innovation Conference convened in Beijing.Centered on the theme“AI-driven Academic:Shaping the Next Frontier”the Conference brought together journal editors,medi...On April 26,2025,the Second Tsinghua Medicine Journal Innovation Conference convened in Beijing.Centered on the theme“AI-driven Academic:Shaping the Next Frontier”the Conference brought together journal editors,medical researchers,and science policy experts to examine how data and artificial intelligence(AI)are reshaping scholarly publishing.Two keynote speeches set the stage:the first analyzed the opportunities for hospital-based research arising from new journal policies,data infrastructure,and enabling technologies;the second introduced the latest advances in general AI and their implications for academic publishing security and integrity.展开更多
Medical institution data compliance is an exogenous product of the digital society,serving as a crucial means to maintain and balance the relationship between data protection and data sharing,as well as individual int...Medical institution data compliance is an exogenous product of the digital society,serving as a crucial means to maintain and balance the relationship between data protection and data sharing,as well as individual interests and public interests.The implementation of the Healthy China Initiative greatly benefits from its practical significance.In practice,data from medical institutions takes varied forms,including personally identifiable data collected before diagnosis and treatment,clinical medical data generated during diagnosis and treatment,medical data collected in public health management,and potential medical data generated in daily life.In the new journey of comprehensively promoting the Chinese path to modernization,it is necessary to clarify the shift from an individual-oriented to a societal-oriented value system,highlighting the reinforcing role of the trust concept.Guided by the principle of minimizing data utilization,the focus is on the new developments and changes in medical institution data in the postpandemic era.This involves a series of measures such as fulfilling the obligation of notification and consent,specifying the scope of data collection and usage,strengthening the standardized use of relevant technical measures,and establishing a sound legal responsibility system for data compliance.Through these measures,a flexible and efficient medical institution data compliance system can be constructed.展开更多
基金supported by the Natural Science Foundation of Hebei Province of China(F2021201052).
文摘With the rapid development of medical data sharing,issues of privacy and ownership have become prominent,which have limited the scale of data sharing.To address the above challenges,we propose a blockchainbased data-sharing framework to ensure data security and encourage data owners to actively participate in sharing.We introduce a reliable attribute-based searchable encryption scheme that enables fine-grained access control of encrypted data and ensures secure and efficient data sharing.The revenue distribution model is constructed based on Shapley value to motivate participants.Additionally,by integrating the smart contract technology of blockchain,the search operation and incentive mechanism are automatically executed.Through revenue distribution analysis,the incentive effect and rationality of the proposed scheme are verified.Performance evaluation shows that,compared with traditional data-sharing models,our proposed framework not only meets data security requirements but also incentivizes more participants to actively participate in data sharing.
基金supported and funded by the National Natural Science Foundation of China(82101079)the Key R&D Program of Jiangsu Province(BE2023836)the National Key Research and Development Program of China(SQ2023YFC2400025).
文摘As the integration of medical big data and artificial intelligence advances,the secure sharing of medical data has become a key driving force for advancing disease research and clinical diagnosis.Federated learning,a distributed approach enabling collaborative data processing without sharing raw data,offers promising solutions to challenges in multi-center medical data sharing.This review summarizes the progress of federated learning in multi-center medical data processing,analyzed from four perspectives:system architectures,data distribution strategies,clinical tasks,and algorithmic models.At the same time,this paper explores the challenges in practical applications,such as data heterogeneity,communication overhead,and privacy concerns.It proposes driving future research development by optimizing algorithms,strengthening privacy protection mechanisms,and enhancing computational efficiency.
基金We are thankful for the funding support fromthe Science and Technology Projects of the National Archives Administration of China(Grant Number 2022-R-031)the Fundamental Research Funds for the Central Universities,Central China Normal University(Grant Number CCNU24CG014).
文摘As the volume of healthcare and medical data increases from diverse sources,real-world scenarios involving data sharing and collaboration have certain challenges,including the risk of privacy leakage,difficulty in data fusion,low reliability of data storage,low effectiveness of data sharing,etc.To guarantee the service quality of data collaboration,this paper presents a privacy-preserving Healthcare and Medical Data Collaboration Service System combining Blockchain with Federated Learning,termed FL-HMChain.This system is composed of three layers:Data extraction and storage,data management,and data application.Focusing on healthcare and medical data,a healthcare and medical blockchain is constructed to realize data storage,transfer,processing,and access with security,real-time,reliability,and integrity.An improved master node selection consensus mechanism is presented to detect and prevent dishonest behavior,ensuring the overall reliability and trustworthiness of the collaborative model training process.Furthermore,healthcare and medical data collaboration services in real-world scenarios have been discussed and developed.To further validate the performance of FL-HMChain,a Convolutional Neural Network-based Federated Learning(FL-CNN-HMChain)model is investigated for medical image identification.This model achieves better performance compared to the baseline Convolutional Neural Network(CNN),having an average improvement of 4.7%on Area Under Curve(AUC)and 7%on Accuracy(ACC),respectively.Furthermore,the probability of privacy leakage can be effectively reduced by the blockchain-based parameter transfer mechanism in federated learning between local and global models.
基金supported by the Guangxi Science and Technology Project(No.AB24010317).
文摘Medical blockchain data-sharing is a technique that employs blockchain technology to facilitate the sharing of electronic medical data.The blockchain is a decentralized digital ledger that ensures data-sharing security,transparency,and traceability through cryptographic technology and consensus algorithms.Consequently,medical blockchain data-sharing methods have garnered significant attention and research efforts.Nevertheless,current methods have different storage and transmission measures for original data in the medical blockchain,resulting in large differences in performance and privacy.Therefore,we divide the medical blockchain data-sharing method into on-chain sharing and off-chain sharing according to the original data storage location.Among them,off-chain sharing can be subdivided into on-cloud sharing and local sharing according to whether the data is moved.Subsequently,we provide a detailed analysis of basic processes and research content for each method.Finally,we summarize the challenges posed by the current methods and discuss future research directions.
基金supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea(NRF-2019S1A5B5A02041334).
文摘The identification and mitigation of anomaly data,characterized by deviations from normal patterns or singularities,stand as critical endeavors in modern technological landscapes,spanning domains such as Non-Fungible Tokens(NFTs),cyber-security,and the burgeoning metaverse.This paper presents a novel proposal aimed at refining anomaly detection methodologies,with a particular focus on continuous data streams.The essence of the proposed approach lies in analyzing the rate of change within such data streams,leveraging this dynamic aspect to discern anomalies with heightened precision and efficacy.Through empirical evaluation,our method demonstrates a marked improvement over existing techniques,showcasing more nuanced and sophisticated result values.Moreover,we envision a trajectory of continuous research and development,wherein iterative refinement and supplementation will tailor our approach to various anomaly detection scenarios,ensuring adaptability and robustness in real-world applications.
基金supported by the Researchers Supporting Program(TUMA-Project-2021–27)Almaarefa University,Riyadh,Saudi ArabiaTaif University Researchers Supporting Project Number(TURSP-2020/161),Taif University,Taif,Saudi Arabia.
文摘Eye state classification acts as a vital part of the biomedical sector,for instance,smart home device control,drowsy driving recognition,and so on.The modifications in the cognitive levels can be reflected via transforming the electro-encephalogram(EEG)signals.The deep learning(DL)models automated extract the features and often showcased improved outcomes over the conventional clas-sification model in the recognition processes.This paper presents an Ensemble Deep Learning with Chimp Optimization Algorithm for EEG Eye State Classifi-cation(EDLCOA-ESC).The proposed EDLCOA-ESC technique involves min-max normalization approach as a pre-processing step.Besides,wavelet packet decomposition(WPD)technique is employed for the extraction of useful features from the EEG signals.In addition,an ensemble of deep sparse autoencoder(DSAE)and kernel ridge regression(KRR)models are employed for EEG Eye State classification.Finally,hyperparameters tuning of the DSAE model takes place using COA and thereby boost the classification results to a maximum extent.An extensive range of simulation analysis on the benchmark dataset is car-ried out and the results reported the promising performance of the EDLCOA-ESC technique over the recent approaches with maximum accuracy of 98.50%.
基金a grant from the“Research Center of the Female Scientific and Medical Colleges”,the Deanship of Scientific Research,King Saud University.
文摘Medical data classification(MDC)refers to the application of classification methods on medical datasets.This work focuses on applying a classification task to medical datasets related to specific diseases in order to predict the associated diagnosis or prognosis.To gain experts’trust,the prediction and the reasoning behind it are equally important.Accordingly,we confine our research to learn rule-based models because they are transparent and comprehensible.One approach to MDC involves the use of metaheuristic(MH)algorithms.Here we report on the development and testing of a novel MH algorithm:IWD-Miner.This algorithm can be viewed as a fusion of Intelligent Water Drops(IWDs)and AntMiner+.It was subjected to a four-stage sensitivity analysis to optimize its performance.For this purpose,21 publicly available medical datasets were used from the Machine Learning Repository at the University of California Irvine.Interestingly,there were only limited differences in performance between IWDMiner variants which is suggestive of its robustness.Finally,using the same 21 datasets,we compared the performance of the optimized IWD-Miner against two extant algorithms,AntMiner+and J48.The experiments showed that both rival algorithms are considered comparable in the effectiveness to IWD-Miner,as confirmed by the Wilcoxon nonparametric statistical test.Results suggest that IWD-Miner is more efficient than AntMiner+as measured by the average number of fitness evaluations to a solution(1,386,621.30 vs.2,827,283.88 fitness evaluations,respectively).J48 exhibited higher accuracy on average than IWD-Miner(79.58 vs.73.65,respectively)but produced larger models(32.82 leaves vs.8.38 terms,respectively).
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number(RGP 2/180/43)Taif University Researchers Supporting Project number(TURSP-2020/346)Taif University,Taif,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:22UQU4340237DSR02.
文摘Recently,medical data classification becomes a hot research topic among healthcare professionals and research communities,which assist in the disease diagnosis and decision making process.The latest developments of artificial intelligence(AI)approaches paves a way for the design of effective medical data classification models.At the same time,the existence of numerous features in the medical dataset poses a curse of dimensionality problem.For resolving the issues,this article introduces a novel feature subset selection with artificial intelligence based classification model for biomedical data(FSS-AICBD)technique.The FSS-AICBD technique intends to derive a useful set of features and thereby improve the classifier results.Primarily,the FSS-AICBD technique undergoes min-max normalization technique to prevent data complexity.In addition,the information gain(IG)approach is applied for the optimal selection of feature subsets.Also,group search optimizer(GSO)with deep belief network(DBN)model is utilized for biomedical data classification where the hyperparameters of the DBN model can be optimally tuned by the GSO algorithm.The choice of IG and GSO approaches results in promising medical data classification results.The experimental result analysis of the FSS-AICBD technique takes place using different benchmark healthcare datasets.The simulation results reported the enhanced outcomes of the FSS-AICBD technique interms of several measures.
基金This project was supported financially by Institution Fund projects under Grant No.(IFPIP-249-145-1442).
文摘Medical data classification becomes a hot research topic in the healthcare sector to aid physicians in the healthcare sector for decision making.Besides,the advances of machine learning(ML)techniques assist to perform the effective classification task.With this motivation,this paper presents a Fuzzy Clustering Approach Based on Breadth-first Search Algorithm(FCA-BFS)with optimal support vector machine(OSVM)model,named FCABFS-OSVM for medical data classification.The proposed FCABFS-OSVM technique intends to classify the healthcare data by the use of clustering and classification models.Besides,the proposed FCABFSOSVM technique involves the design of FCABFS technique to cluster the medical data which helps to boost the classification performance.Moreover,the OSVM model investigates the clustered medical data to perform classification process.Furthermore,Archimedes optimization algorithm(AOA)is utilized to the SVM parameters and boost the medical data classification results.A wide range of simulations takes place to highlight the promising performance of the FCABFS-OSVM technique.Extensive comparison studies reported the enhanced outcomes of the FCABFS-OSVM technique over the recent state of art approaches.
基金This research work was funded by Institutional fund projects under Grant No.(IFPHI-038-156-2020)Therefore,authors gratefully acknowledge technical and financial support from Ministry of Education and King Abdulaziz University,DSR,Jeddah,Saudi Arabia.
文摘This study presents a novelmethod to detect themedical application based on Quantum Computing(QC)and a few Machine Learning(ML)systems.QC has a primary advantage i.e.,it uses the impact of quantum parallelism to provide the consequences of prime factorization issue in a matter of seconds.So,this model is suggested for medical application only by recent researchers.A novel strategy i.e.,Quantum KernelMethod(QKM)is proposed in this paper for data prediction.In this QKM process,Linear Tunicate Swarm Algorithm(LTSA),the optimization technique is used to calculate the loss function initially and is aimed at medical data.The output of optimization is either 0 or 1 i.e.,odd or even in QC.From this output value,the data is identified according to the class.Meanwhile,the method also reduces time,saves cost and improves the efficiency by feature selection process i.e.,Filter method.After the features are extracted,QKM is deployed as a classification model,while the loss function is minimized by LTSA.The motivation of the minimal objective is to remain faster.However,some computations can be performed more efficiently by the proposed model.In testing,the test data was evaluated by minimal loss function.The outcomes were assessed in terms of accuracy,computational time,and so on.For this,databases like Lymphography,Dermatology,and Arrhythmia were used.
文摘With the exponential developments of wireless networking and inexpensive Internet of Things(IoT),a wide range of applications has been designed to attain enhanced services.Due to the limited energy capacity of IoT devices,energy-aware clustering techniques can be highly preferable.At the same time,artificial intelligence(AI)techniques can be applied to perform appropriate disease diagnostic processes.With this motivation,this study designs a novel squirrel search algorithm-based energy-aware clustering with a medical data classification(SSAC-MDC)model in an IoT environment.The goal of the SSAC-MDC technique is to attain maximum energy efficiency and disease diagnosis in the IoT environment.The proposed SSAC-MDC technique involves the design of the squirrel search algorithm-based clustering(SSAC)technique to choose the proper set of cluster heads(CHs)and construct clusters.Besides,the medical data classification process involves three different subprocesses namely pre-processing,autoencoder(AE)based classification,and improved beetle antenna search(IBAS)based parameter tuning.The design of the SSAC technique and IBAS based parameter optimization processes show the novelty of the work.For show-casing the improved performance of the SSAC-MDC technique,a series of experiments were performed and the comparative results highlighted the supremacy of the SSAC-MDC technique over the recent methods.
文摘With the improvement of current online communication schemes,it is now possible to successfully distribute and transport secured digital Content via the communication channel at a faster transmission rate.Traditional steganography and cryptography concepts are used to achieve the goal of concealing secret Content on a media and encrypting it before transmission.Both of the techniques mentioned above aid in the confidentiality of feature content.The proposed approach concerns secret content embodiment in selected pixels on digital image layers such as Red,Green,and Blue.The private Content originated from a medical client and was forwarded to a medical practitioner on the server end through the internet.The K-Means clustering principle uses the contouring approach to frame the pixel clusters on the image layers.The content embodiment procedure is performed on the selected pixel groups of all layers of the image using the Least Significant Bit(LSB)substitution technique to build the secret Content embedded image known as the stego image,which is subsequently transmitted across the internet medium to the server end.The experimental results are computed using the inputs from“Open-Access Medical Image Repositories(aylward.org)”and demonstrate the scheme’s impudence as the Content concealing procedure progresses.
文摘In order to secure the massive heterogeneous medical data for the complex scenarios and improve the information sharing efficiency in healthcare system,a distributed medical data ledger model(DMDL)is proposed in this paper.This DMDL model has adopted the blockchain technology including the function decoupling,the distributed consensus,smart contract as well as multi-channel communication structure of consortium blockchain.The DMDL model not only has high adaptability,but also meets the requirements of the medical treatment processes which generally involve multientities,highly private information and secure transaction.The steps for processing the medical data are also introduced.Additionally,the methods for the definition and application of the DMDL model are presented for three specific medical scenarios,i.e.,the management of the heterogeneous data,copyright protection for medical data and the secure utilization of sensitive data.The advantage of the proposed DMDL model is demonstrated by comparing with the models which are being currently adopted in healthcare system.
文摘The Corona Virus Disease 2019(COVID-19) pandemic has taught us many valuable lessons regarding the importance of our physical and mental health. Even with so many technological advancements, we still lag in developing a system that can fully digitalize the medical data of each individual and make it readily accessible for both the patient and health worker at any point in time. Moreover, there are also no ways for the government to identify the legitimacy of a particular clinic. This study merges modern technology with traditional approaches,thereby highlighting a scenario where artificial intelligence(AI) merges with traditional Chinese medicine(TCM), proposing a way to advance the conventional approaches. The main objective of our research is to provide a one-stop platform for the government, doctors,nurses, and patients to access their data effortlessly. The proposed portal will also check the doctors’ authenticity. Data is one of the most critical assets of an organization, so a breach of data can risk users’ lives. Data security is of primary importance and must be prioritized. The proposed methodology is based on cloud computing technology which assures the security of the data and avoids any kind of breach. The study also accounts for the difficulties encountered in creating such an infrastructure in the cloud and overcomes the hurdles faced during the project, keeping enough room for possible future innovations. To summarize, this study focuses on the digitalization of medical data and suggests some possible ways to achieve it. Moreover, it also focuses on some related aspects like security and potential digitalization difficulties.
基金supported by the National Natural Science Foundation of China(grant no.82071928)the Program of Shanghai Academic/Technology Research Leader(grant no.23XD1401300).
文摘The vast potential of medical big data to enhance healthcare outcomes remains underutilized due to privacy concerns,which restrict cross-center data sharing and the construction of diverse,large-scale datasets.To address this challenge,we developed a deep generative model aimed at synthesizing medical data to overcome data sharing barriers,with a focus on breast ultrasound(US)image synthesis.Specifically,we introduce CoLDiT,a conditional latent diffusion model with a transformer backbone,to generate US images of breast lesions across various Breast Imaging Reporting and Data System(BI-RADS)categories.Using a training dataset of 9,705 US images from 5,243 patients across 202 hospitals with diverse US systems,CoLDiT generated breast US images without duplicating private information,as confirmed through nearest-neighbor analysis.Blinded reader studies further validated the realism of these images,with area under the receiver operating characteristic curve(AUC)scores ranging from 0.53 to 0.77.Additionally,synthetic breast US images effectively augmented the training dataset for BI-RADS classification,achieving performance comparable to that using an equal-sized training set comprising solely real images(P=0.81 for AUC).Our findings suggest that synthetic data,such as CoLDiT-generated images,offer a viable,privacy-preserving solution to facilitate secure medical data sharing and advance the utilization of medical big data.
文摘With the rapid evolution of artificial intelligence(AI)technologies,the medical industry is undergoing a profound transformation driven by data intelligence.As the foundational element for intelligent diagnosis,precision prevention,and public health governance,medical data is characterized by massive volume,complex structure,diverse sources,high dimensionality,strong privacy,and high timeliness.Traditional data analysis methods are no longer sufficient to meet the comprehensive requirements of data security,intelligent processing,and decision support.Through techniques such as machine learning,deep learning,natural language processing,and multimodal fusion,AI provides robust technical support for medical data cleaning,governance,mining,and application.At the data level,intelligent algorithms enable the standardization,structuring,and interoperability of medical data,promoting information sharing across medical systems.At the model level,AI supports auxiliary diagnosis and precision treatment through image recognition,medical record analysis,and knowledge graph construction.At the system level,intelligent decision-support platforms continuously enhance the efficiency and accuracy of healthcare services.However,the widespread adoption of AI in medicine still faces challenges such as privacy protection,data security,model interpretability,and the lack of unified industry standards.Based on a systematic review of AI’s key supporting technologies in medical data processing and application,this paper focuses on the compliance challenges and adaptation strategies during industry integration and proposes an adaptation framework centered on“technological trustworthiness,data security,and industry collaboration.”The study provides theoretical and practical insights for promoting the standardized and sustainable development of AI in the healthcare industry.
基金supported by the National Natural Science Foundation of China(Grant No.72374081)the Key Research and Development Project of the Department of Science and Technology of Jilin Province(Grant No.20240304164SF).
文摘Purpose:Explore the factors affecting medical data sharing in clinical research scenarios from the user’s perspective,reveal the differences between different user groups,and deepen the understanding of medical data sharing mechanisms.Design/methodology/approach:By integrating the UTAUT model,trust theory and self-efficacy theory,introducing the concepts of data transparency and individual innovation,and combining internal and external motivators,we constructed a conceptual model of medical data users’sharing behavior in clinical research scenarios.We conducted empirical research by collecting 360 pieces of first-hand data from clinical researchers.Findings:Among the internal motivators,effort expectation had a higher impact on sharing intention than performance expectation,individual innovation and self-efficacy had a higher impact on sharing behavior than trust.Trust does not show a significant impact on sharing intention,but it has a significant positive influence on sharing behavior.Among the external motivators,community influence and data transparency both positively affect sharing intention.In addition,users with different working years,professional status,data level needs,and different sharing experiences showed significant differences in healthcare data sharing.Research limitations:Our sample of clinical researchers from China was used as empirical data.Further research is needed to examine the generality of the study findings.Practical implications:The findings enhance healthcare data stakeholders’understanding of healthcare data sharing in clinical research scenarios and provide theoretical and practical insights for relevant researchers.Originality/value:In this study,the UTAUT model,trust theory and self-efficacy theory were integrated and applied to clinical research scenarios for the first time,and the concepts of data transparency and individual innovation were introduced,and the CRS-USB conceptual model was constructed and validated to extend the UTAUT model.
基金This work was supported by the National Key Research and Development Program of China under Grant 2021YFF0704102.
文摘Vertical Federated Learning(VFL)has many applications in the field of smart healthcare with excellent performance.However,current VFL systems usually primarily focus on the privacy protection during model training,while the preparation of training data receives little attention.In real-world applications,like smart healthcare,the process of the training data preparation may involve some participant's intention which could be privacy information for this partici-pant.To protect the privacy of the model training intention,we describe the idea of Intention-Hiding Vertical Feder-ated Learning(IHVFL)and illustrate a framework to achieve this privacy-preserving goal.First,we construct two secure screening protocols to enhance the privacy protection in feature engineering.Second,we implement the work of sample alignment bases on a novel private set intersection protocol.Finally,we use the logistic regression algorithm to demonstrate the process of IHVFL.Experiments show that our model can perform better efficiency(less than 5min)and accuracy(97%)on Breast Cancer medical dataset while maintaining the intention-hiding goal.
文摘On April 26,2025,the Second Tsinghua Medicine Journal Innovation Conference convened in Beijing.Centered on the theme“AI-driven Academic:Shaping the Next Frontier”the Conference brought together journal editors,medical researchers,and science policy experts to examine how data and artificial intelligence(AI)are reshaping scholarly publishing.Two keynote speeches set the stage:the first analyzed the opportunities for hospital-based research arising from new journal policies,data infrastructure,and enabling technologies;the second introduced the latest advances in general AI and their implications for academic publishing security and integrity.
文摘Medical institution data compliance is an exogenous product of the digital society,serving as a crucial means to maintain and balance the relationship between data protection and data sharing,as well as individual interests and public interests.The implementation of the Healthy China Initiative greatly benefits from its practical significance.In practice,data from medical institutions takes varied forms,including personally identifiable data collected before diagnosis and treatment,clinical medical data generated during diagnosis and treatment,medical data collected in public health management,and potential medical data generated in daily life.In the new journey of comprehensively promoting the Chinese path to modernization,it is necessary to clarify the shift from an individual-oriented to a societal-oriented value system,highlighting the reinforcing role of the trust concept.Guided by the principle of minimizing data utilization,the focus is on the new developments and changes in medical institution data in the postpandemic era.This involves a series of measures such as fulfilling the obligation of notification and consent,specifying the scope of data collection and usage,strengthening the standardized use of relevant technical measures,and establishing a sound legal responsibility system for data compliance.Through these measures,a flexible and efficient medical institution data compliance system can be constructed.
